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Surface Electromyography (SEMG) is recorded using surface electrodes placed on the biceps brachii muscles during continuous flexion and extension of the elbow joint. SEMG recording is done with elbow angle changing at different angular velocities. The obtained SEMG signals are segmented into 250-millisecond duration windows using disjoint windowing technique. Two time-domain parameters, Integrated Electromyography (IEMG) and Zero crossing (ZC) are derived from each windowed SEMG signals. The obtained value of IEMG and ZC are fed as the inputs to the Multiple Input Multiple Output (MIMO) model for the estimation of elbow angular displacement and elbow angular velocity. In this work three Multiple Input Multiple Output (MIMO) nonlinear black box model are developed using a Nonlinear Auto Regressive with eXogenous inputs (NARX) structure: (1) Multi-Layered Perceptron Neural Network (MLPNN) model based on NARX input, (2) Elman neural network model based on NARX input and (3) Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on NARX input. The results obtained from the three different models are compared using statistical parameters like regression coefficient and root mean square error (RSME). Based on this comparison the paper proposes that the estimation of elbow kinematics using ANFIS NARX model gives more accurate results when compared with Elman NARX model and Multi-Layered Perceptron Neural Network (MLPNN) NARX model.<\/jats:p>","DOI":"10.3233\/jifs-16070","type":"journal-article","created":{"date-parts":[[2016,11,25]],"date-time":"2016-11-25T10:53:01Z","timestamp":1480071181000},"page":"791-805","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Comparative study on estimation of elbow kinematics based on EMG time domain parameters using neural network and\u00a0ANFIS NARX model"],"prefix":"10.1177","volume":"32","author":[{"given":"Retheep","family":"Raj","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Institute of Technology Calicut, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.S.","family":"Sivanandan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Institute of Technology Calicut, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,11,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.03.014"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2007.07.009"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/86.895950"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1249\/01.mss.0000233794.31659.6d"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.11.009"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.192"},{"key":"e_1_3_2_8_2","first-page":"716","article-title":"EMG based classification of basic hand movements based on time - frequency features","author":"Sapsanis C.","year":"2013","unstructured":"SapsanisC., GeorgoulasG., TzesA. and MemberS., EMG based classification of basic hand movements based on time - frequency features, IEEE Int Conf Control Autom (2013), 716\u2013722.","journal-title":"IEEE Int Conf Control Autom"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/24\/2\/307"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2008.2005485"},{"key":"e_1_3_2_11_2","first-page":"1","article-title":"Hand motion detection from EMG signals by using ANN based classifier for human computer interaction","author":"Ahsan M.R.","year":"2011","unstructured":"AhsanM.R., IbrahimyM.I. and KhalifaO.O., Hand motion detection from EMG signals by using ANN based classifier for human computer interaction, Fourth Int Conf Model Simul Appl Optim (2011), 1\u20136.","journal-title":"Fourth Int Conf Model Simul Appl Optim"},{"key":"e_1_3_2_12_2","unstructured":"NellesO. 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